Model quality assurance P&C - Model Risk Rating Assistant

Purpose

Model quality assurance P&C - Model Risk Rating Assistant is designed to support model quality assurance by applying independent ML models to benchmark the predictive accuracy and calibration of the primary actuarial model in an insurance context.

Primary users

The primary user is not specified. The available information identifies the Team/BU as AQS and the owner as Ronan Davit.

Where it fits (process/stage/trigger)

This agent fits within the model quality assurance and model risk rating process for P&C insurance models, particularly when the primary actuarial model needs to be independently benchmarked against model outputs, historical policy and claims data, and benchmark results.

Key capabilities / workflow

The agent uses independent ML models to benchmark the primary actuarial model, focusing on predictive accuracy and calibration. The workflow centers on using available model outputs, historical policy and claims data, and benchmark results to support model risk rating activities.

Inputs

Formal inputs are not specified. The provided dataset information includes model outputs, historical policy and claims data, and benchmark results.

Outputs / Deliverables

Formal outputs are not specified. Based on the provided use case, the agent supports benchmarking of predictive accuracy and calibration for the primary actuarial model, with benchmark results identified as part of the available dataset context.

Value

The agent provides value by supporting independent model quality assurance for insurance actuarial models, helping benchmark the primary model’s predictive accuracy and calibration as part of model risk rating activities.